No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by steel-dev · Agent Tool · ★ 7.7k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is steel-browser safe to install? View the security audit →
Steel The open-source browser API for AI agents & apps. The best way to build live web agents and browser automation tools. Get Started · Documentation · Website · Cookbook ✨ Highlights [Steel.d
| Stars | 7,678 |
| Forks | 983 |
| Language | TypeScript |
| Category | Agent Tool |
| License | Apache-2.0 |
| Quality Score | 68.2153290155664/100 |
| Open Issues | 69 |
| Last Updated | 2026-09-21 |
| Created | 2024-11-01 |
| Platforms | browser, node |
| Est. Tokens | ~17k |
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steel-browser is 🔥 Open Source Browser API for AI Agents & Apps. Steel Browser is a batteries-included browser sandbox that lets you automate the web without worrying about infrastructure.. It is categorized as a Agent Tool with 7.7k GitHub stars.
steel-browser is primarily written in TypeScript. It covers topics such as ai, ai-agents, ai-tools.
You can find installation instructions and usage details in the steel-browser GitHub repository at github.com/steel-dev/steel-browser. The project has 7.7k stars and 983 forks, indicating an active community.
steel-browser is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to steel-browser on Agent Skills Hub include nanobrowser, bytebot, hexstrike-ai. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: